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Crate scirs2_io

Crate scirs2_io 

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§SciRS2 IO - Scientific Data Input/Output

scirs2-io provides comprehensive file I/O capabilities for scientific computing, supporting MATLAB, NetCDF, HDF5, CSV, WAV, image formats, and more, with streaming, compression, async support, and database connectivity.

§🎯 Key Features

  • SciPy Compatibility: Similar to scipy.io for MATLAB, WAV, ARFF files
  • Multiple Formats: MATLAB (.mat), NetCDF, HDF5, CSV, WAV, images (PNG, JPEG, TIFF)
  • Matrix Market: Sparse matrix exchange format
  • Streaming I/O: Memory-efficient reading/writing of large datasets
  • Compression: GZIP, ZSTD, LZ4, BZIP2 for data compression
  • Async I/O: Non-blocking operations with tokio
  • Database: SQL/NoSQL connectivity (PostgreSQL, MongoDB, InfluxDB)

§📦 Module Overview

SciRS2 ModuleSciPy EquivalentDescription
matlabscipy.io.loadmat, savematMATLAB .mat file I/O
wavfilescipy.io.wavfileWAV audio file I/O
netcdfscipy.io.netcdfNetCDF scientific data format
matrix_marketscipy.io.mmread, mmwriteMatrix Market sparse format
csv-CSV with type conversion
image-PNG, JPEG, BMP, TIFF image I/O

§🚀 Quick Start

[dependencies]
scirs2-io = "0.1.5"
use scirs2_io::csv::{read_csv, CsvReaderConfig};

// Read CSV file
let config = CsvReaderConfig {
    has_header: true,
    delimiter: ',',
    ..Default::default()
};
let (headers, data) = read_csv("data.csv", Some(config)).unwrap();

§🔒 Version: 0.1.5 (January 15, 2026)

§Modules

  • arff: Support for ARFF (Attribute-Relation File Format) files
  • compression: Utilities for data compression and decompression
  • csv: Support for CSV (Comma-Separated Values) files
  • image: Support for image file formats (PNG, JPEG, BMP, TIFF)
  • matlab: Support for MATLAB (.mat) files
  • matrix_market: Support for Matrix Market sparse and dense matrix files
  • netcdf: Support for NetCDF scientific data files
  • serialize: Utilities for data serialization and deserialization
  • validation: Utilities for data validation and integrity checking
  • wavfile: Support for WAV audio files
  • error: Error types for the IO module
  • fortran: Support for Fortran unformatted files

Re-exports§

pub use advanced_coordinator::AdaptiveImprovements;
pub use advanced_coordinator::AdvancedCoordinator;
pub use advanced_coordinator::AdvancedStatistics;
pub use advanced_coordinator::IntelligenceLevel;
pub use advanced_coordinator::PerformanceIntelligenceStats;
pub use advanced_coordinator::ProcessingResult;
pub use advanced_coordinator::QualityMetrics;
pub use advanced_coordinator::StrategyType;
pub use enhanced_algorithms::AdvancedPatternAnalysis;
pub use enhanced_algorithms::AdvancedPatternRecognizer;
pub use enhanced_algorithms::DataCharacteristics;
pub use enhanced_algorithms::EmergentPattern;
pub use enhanced_algorithms::MetaPattern;
pub use enhanced_algorithms::OptimizationRecommendation;
pub use enhanced_algorithms::SynergyType;
pub use arff::read_arff;
pub use arff::write_arff;
pub use arff::ArffData;
pub use arff::ArffValue;
pub use arff::AttributeType;
pub use arff::SparseArffData;
pub use arff::SparseInstance;
pub use bmp::read_bmp;
pub use bmp::write_bmp;
pub use bmp::BmpImage;
pub use columnar::filter_table;
pub use columnar::read_columnar;
pub use columnar::read_columnar_with_columns;
pub use columnar::select_columns;
pub use columnar::split_into_row_groups;
pub use columnar::write_columnar;
pub use columnar::write_columnar_with_options;
pub use columnar::Column;
pub use columnar::ColumnData;
pub use columnar::ColumnStats;
pub use columnar::ColumnTypeTag;
pub use columnar::ColumnarTable;
pub use columnar::ColumnarWriteOptions;
pub use columnar::EncodingType;
pub use columnar::Predicate;
pub use columnar::RowGroup;
pub use columnar::RowGroupConfig;
pub use columnar::TableStats;
pub use netcdf_lite::NcDataType;
pub use netcdf_lite::NcDimension;
pub use netcdf_lite::NcFile;
pub use netcdf_lite::NcValue;
pub use netcdf_lite::NcVariable;
pub use npy::read_npy;
pub use npy::read_npz;
pub use npy::write_npy;
pub use npy::write_npy_f32;
pub use npy::write_npy_f64;
pub use npy::write_npy_f64_2d;
pub use npy::write_npy_i32;
pub use npy::write_npy_i64;
pub use npy::write_npz;
pub use npy::ByteOrder;
pub use npy::NpyArray;
pub use npy::NpyDtype;
pub use npy::NpyHeader;
pub use npy::NpzArchive;
pub use wavfile::read_wav;
pub use wavfile::write_wav;
pub use wavfile::write_wav_config;
pub use wavfile::WavFormat;
pub use wavfile::WavHeader;
pub use wavfile::WavOutputFormat;
pub use wavfile::WavWriteConfig;

Modules§

advanced_coordinator
Advanced Mode Coordinator - Unified Intelligence for I/O Operations
arff
ARFF (Attribute-Relation File Format) handling module
async_io
Async I/O support for streaming capabilities
bmp
Pure Rust BMP image file format (24-bit uncompressed)
columnar
Pure Rust columnar storage format
compression
Data compression module
csv
CSV (Comma-Separated Values) file format module
database
Database connectivity
distributed
Distributed I/O processing
enhanced_algorithms
Enhanced algorithms for Advanced Mode
error
Error types for the IO module
formats
Domain-specific file formats
fortran
Fortran unformatted file format module
gpu
GPU-accelerated I/O operations
harwell_boeing
Harwell-Boeing sparse matrix format module
hdf5
HDF5 file format module
idl
IDL (Interactive Data Language) save file format module
image
Image file format module
jsonl
JSON Lines (NDJSON) format support
matlab
MATLAB file format (.mat) handling module
matrix_market
Matrix Market file format module
metadata
Advanced metadata management
ml_framework
Machine learning framework compatibility
mmap
Data pipeline APIs
netcdf
NetCDF file format module
netcdf_lite
Pure Rust NetCDF Classic format reader/writer
network
Network I/O and cloud storage integration
neural_adaptive_io
Neural-adaptive I/O optimization with advanced-level intelligence
npy
NumPy NPY/NPZ binary file format support
out_of_core
Out-of-core processing for terabyte-scale datasets
parquet
Apache Parquet columnar file format module
pipeline
Data pipeline APIs
quantum_inspired_io
Quantum-inspired I/O processing algorithms with advanced capabilities
realtime
Real-time data streaming protocols
serialize
Data serialization utilities
simd_io
SIMD-accelerated I/O operations
sparse
Comprehensive sparse matrix format support
streaming
Streaming and iterator interfaces for large data handling
thread_pool
Thread pool for parallel I/O operations
validation
Data validation and integrity checking module
visualization
Visualization tool integration
wavfile
WAV file format handling module
workflow
Workflow automation tools
zero_copy
Zero-copy I/O optimizations

Macros§

pipeline
Pipeline configuration DSL for easier pipeline creation
stage
Stage creation DSL